The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Water, often received via rain or other precipitation, is an essential element to life. For farmers, rainfall is a large factor in determining how much water a crop receives, thereby altering the potential yield for the crop. While rainfall has many positive effects, such as giving life to crops, large quantities of rainfall can also have severe repercussions, such as by causing floods or resulting in standing or ponding water that can inundate seedlings or mature crops. Thus, accurate measurements of rainfall can be extremely important, both to maximize gains from the rainfall and minimize risks from an overabundance of rainfall.
Rainfall is generally measured using one of two approaches. One approach for measuring rainfall involves utilizing physically placed rain gauges. The rain gauges are set at a variety of locations and are used to gather precipitation and measure the amount of precipitation received at the rain gauge over a period of time. While rain gauges create accurate measurements of the amount of precipitation received at the rain gauge, rain gauge data is only available where a rain gauge has been physically placed. Precipitation amounts in non-gauge locations may be inferred from the measurements at surrounding gauge locations, but the inferred precipitation amounts do not contain the same levels of accuracy as the gauge measurements.
A second approach to measuring rainfall involves utilizing radar data to calculate the rainfall. Generally, a polarized beam of energy is emitted from a radar device in a particular direction. The beam travels un-disturbed before encountering a volume of air containing hydrometeors, such as rainfall, snowfall, or hail, which causes the beam to scatter energy back to a radar receiver. Based on the amount of time it takes for a radar beam to return, the distance between the radar device and the volume of air containing hydrometeors is computed. The amount of energy that is received by the radar, also known as the reflectivity, is used to compute the rainfall rate. Often, the relationship between the reflectivity and the actual rainfall rate is modeled through the Z-R transformation:Z=aRb where Z is the reflectivity and R is the actual rainfall rate. The parameters for the Z-R transformation may be identified through measurements for rain gauges for a particular area and/or type of storm.
A drawback with using radar reflectivity to measure the rainfall rate is that the radar reflectivity at best creates an estimate of the actual rainfall. While radar reflectivity is generally understood to be directly related to the rainfall rate, a wide variety of atmospheric conditions are capable of leading to the same reflectivity, yet producing different rainfall rates. The differences in drop sizes specifically can lead to variations in the rainfall rate while producing the same reflectivity. For example, a small number of large drop sizes will produce the same reflectivity data as a large number of smaller rainfall drops, but a large number of smaller drop sizes will generally produce more precipitation on the ground than a small number of large drop sizes. Often, these inaccuracies are similar across nearby locations due to similarities in storm sizes and distances between the location of the precipitation and the radar devices.
Many hydrologists attempt to solve the inaccuracies in measurements of rainfall rates with radar devices by employing calibration techniques to ensure that the radar measurements that are received are as accurate as possible. The persistent problem is that the actual error in the rainfall rates is not measured or computed. Even if estimates of rainfall rates can be produced with higher accuracy through calibration techniques, it is still important to be able to determine and present the full range of possible precipitation values. For example, if it is known that a river will flood if it receives over an inch of rain, then an estimate of 0.9 inches of rain may lead hydrologists to the false conclusion that the river will not flood. On the other hand, a probabilistic estimate that includes the possible range of precipitation values with corresponding likelihoods would allow a hydrologist to determine the probability that the river will flood.
Additionally, the amount of rainfall a crop has received is extremely important in modeling the growth of a crop. The amount of water a field receives not only affects the availability of water to the crop, but the availability of other elements, such as nitrogen and potassium, to the crop. As a crop's growth can be widely dependent on the availability of water and other chemicals, variances in the amount of rainfall received by a field can lead to variances in a model of the growth of a crop. If these variances are not understood and presented to a farmer, the farmer may not be able to make informed decisions with respect to the crop.